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Advanced traffic monitoring for sustainable traffic management: Experiences and results of five years of collaborative research in the Netherlands

机译:用于可持续交通管理的高级交通监控:荷兰五年合作研究的经验和成果

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摘要

This study overviews the scientific and practical results obtained within the advanced traffic monitoring (ATMO) project (www.atmo.tudelft.nl) from its inception in 2004 until its final year 2009. ATMO is a 2.5 million Euros research project in which universities, public bodies and the industry collaboratively addressed the question how to translate large amounts of raw traffic data from all sorts of sensors and systems into useable and meaningful information for traffic information, and sustainable traffic management and control. In the context of sustainability, traffic monitoring entails not just observing or estimating 'classic' traffic data and information, such as average speed, volume or delay and travel time, but also more diffuse concepts such as travel time reliability, traffic safety and environmentally related quantities. In five PhD tracks many scientific contributions were made such as new hybrid methods and models for traffic and travel time prediction, online traffic simulation models and new travel time reliability measures, to name a few. ATMO also demonstrated that bridging the gap between science and practice in the field of traffic monitoring requires entrepreneurial academics, sharing of data and results and tailor-made post-academic courses and education.
机译:这项研究概述了高级交通监控(ATMO)项目(www.atmo.tudelft.nl)从2004年启动到2009年最后一年所获得的科学和实践成果。ATMO是一项耗资250万欧元的研究项目,其中大学,公共机构和行业共同解决了以下问题:如何将来自各种传感器和系统的大量原始交通数据转换为可用于交通信息的有意义的信息,以及可持续的交通管理和控制。在可持续性的背景下,交通监控不仅需要观察或估计“经典”交通数据和信息,例如平均速度,交通量或延误以及出行时间,还需要散布更多概念,例如出行时间可靠性,交通安全和与环境相关的信息数量。在五个博士学位课程中,做出了许多科学贡献,例如用于交通和出行时间预测的新混合方法和模型,在线交通模拟模型以及新的出行时间可靠性度量等。 ATMO还证明,要在交通监控领域弥合科学与实践之间的差距,就需要企业家学者,共享数据和结果以及量身定制的学术课程和教育。

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